Detection of anomalies in the interior of an autonomous vehicle

ABSTRACT

An autonomous vehicle includes a microphone sensing sounds in the interior of the vehicle. The output of the interior microphone is processed according to an unsupervised machine learning model such that anomalies are indicated by the model. In response to detection of an anomaly, a remote dispatcher is notified, who may then dismiss the anomaly or transmit an instruction to the vehicle to alter its trajectory. The output of an exterior microphone and infotainment system may be removed from the output of the interior microphone prior to processing. An anomaly may be found to occur in response to detecting speaking of a keyword in the output of the interior microphone.

BACKGROUND Field of the Invention

This invention relates to a sensor system and method for an autonomousvehicle.

Background of the Invention

Recent announcements from different auto companies (including Ford)predict fully autonomous cars (SAE level 4) to be commercially availablein the next few years. The absence of a driver raises several problemsthat were not expected in a non-autonomous vehicle or an autonomousvehicle with a safety driver. Particularly for the ride-sharing andride-hailing activities (where the passenger does not own the vehicle).

The system and methods disclosed herein provides an improved approachfor promoting safety for autonomous vehicles in the absence of a safetydriver.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered limiting of its scope, the invention will be describedand explained with additional specificity and detail through use of theaccompanying drawings, in which:

FIG. 1A is a schematic block diagram of a system for implementingembodiments of the invention;

FIG. 1B is a schematic block diagram of a vehicle including interiorsensors for implementing embodiments of the invention;

FIG. 2 is a schematic block diagram of an example computing devicesuitable for implementing methods in accordance with embodiments of theinvention;

FIG. 3 is a process flow diagram of a method for detecting anomalies inthe interior of an autonomous vehicle in accordance with an embodimentof the present invention; and

FIG. 4 is a process flow diagram of a method for training a machinelearning model to handle anomalies in the interior of a vehicle inaccordance with an embodiment of the present invention.

DETAILED DESCRIPTION

Referring to FIGS. 1A and 1B, a vehicle 100 (see FIG. 1B) may house acontroller 102. The vehicle 100 may include any vehicle known in theart. The vehicle 100 may have all of the structures and features of anyvehicle known in the art including, wheels, a drive train coupled to thewheels, an engine coupled to the drive train, a steering system, abraking system, and other systems known in the art to be included in avehicle.

As discussed in greater detail herein, the controller 102 may performautonomous navigation and collision avoidance. The controller 102 mayreceive one or more outputs from one or more exterior sensors 104. Forexample, one or more cameras 106 a may be mounted to the vehicle 100 andoutput image streams received to the controller 102. The controller 102may receive one or more audio streams from one or more microphones 106 bmounted external to the vehicle or otherwise receiving sound from theexterior of the vehicle. For example, external microphones may have anopen air channel between a sound sensor and the exterior of the vehicleand are preferably not mounted within the cabin of the vehicle 100. Theone or more microphones 106 b or microphone arrays 106 b may be mountedto the vehicle 100 and output audio streams to the controller 102. Themicrophones 106 b may include directional microphones having asensitivity that varies with angle. In such embodiments, the directionthat the microphones sense may be directed outward from the vehicle 100.

The exterior sensors 104 may include sensors such as RADAR (RadioDetection and Ranging) 106 c, LIDAR (Light Detection and Ranging) 106 d,SONAR (Sound Navigation and Ranging) 106 e, and the like.

The controller 102 may execute an autonomous operation module 108 thatreceives the outputs of the exterior sensors 104. The autonomousoperation module 108 may include an obstacle identification module 110a, a collision prediction module 110 b, and a decision module 110 c. Theobstacle identification module 110 a analyzes the outputs of theexterior sensors and identifies potential obstacles, including people,animals, vehicles, buildings, curbs, and other objects and structures.In particular, the obstacle identification module 110 a may identifyvehicle images in the sensor outputs.

The collision prediction module 110 b predicts which obstacle images arelikely to collide with the vehicle 100 based on its current trajectoryor current intended path. The collision prediction module 110 b mayevaluate the likelihood of collision with objects identified by theobstacle identification module 110 a. The decision module 110 c may makea decision to stop, accelerate, turn, etc. in order to avoid obstacles.The manner in which the collision prediction module 110 b predictspotential collisions and the manner in which the decision module 110 ctakes action to avoid potential collisions may be according to anymethod or system known in the art of autonomous vehicles.

The decision module 110 c may control the trajectory of the vehicle byactuating one or more actuators 112 controlling the direction and speedof the vehicle 100. For example, the actuators 112 may include asteering actuator 114 a, an accelerator actuator 114 b, and a brakeactuator 114 c. The configuration of the actuators 114 a-114 c may beaccording to any implementation of such actuators known in the art ofautonomous vehicles.

In embodiments disclosed herein, the autonomous operation module 108 mayperform autonomous navigation to a specified location, autonomousparking, and other automated driving activities known in the art.

The autonomous operation module 108 may further include an anomalydetection module 110 d. The anomaly detection module 110 d detectsanomalies occurring in the interior of the vehicle and takes one or moreactions based thereon. The operation of the anomaly detection module 110d may be understood with respect to FIGS. 3 and 4 described below.

The anomaly detection module 110 d may take as inputs the outputs of oneor more interior sensors 116, such as one or more cameras 118 a and oneor more interior microphones 118 b.

As shown in FIG. 1B, one or more cameras 118 a may be positioned andoriented in the vehicle to have all seating positions in the field ofview of at least one of the cameras 118 a. Other areas of the interiorof the vehicle may also be in the field of at least one of the cameras118 a. Microphones 118 b may also be distributed throughout the interiorin order to detect sounds from occupants of the vehicle. An externalmicrophone 106 b may be used as a reference to distinguish betweenexternal and internal sounds as discussed below.

The controller 102 may be in data communication with a server 120, suchas by means of a network 122 that may include any wired or wirelessnetwork connection, including a cellular data network connection. Themethods disclosed herein may be implemented by the server 120, thecontroller 102, or a combination of the two.

FIG. 2 is a block diagram illustrating an example computing device 200.Computing device 200 may be used to perform various procedures, such asthose discussed herein. The controller 102 and server system 120 mayhave some or all of the attributes of the computing device 200.

Computing device 200 includes one or more processor(s) 202, one or morememory device(s) 204, one or more interface(s) 206, one or more massstorage device(s) 208, one or more Input/Output (I/O) device(s) 210, anda display device 230 all of which are coupled to a bus 212. Processor(s)202 include one or more processors or controllers that executeinstructions stored in memory device(s) 204 and/or mass storagedevice(s) 208. Processor(s) 202 may also include various types ofcomputer-readable media, such as cache memory.

Memory device(s) 204 include various computer-readable media, such asvolatile memory (e.g., random access memory (RAM) 214) and/ornonvolatile memory (e.g., read-only memory (ROM) 216). Memory device(s)204 may also include rewritable ROM, such as Flash memory.

Mass storage device(s) 208 include various computer readable media, suchas magnetic tapes, magnetic disks, optical disks, solid-state memory(e.g., Flash memory), and so forth. As shown in FIG. 2, a particularmass storage device is a hard disk drive 224. Various drives may also beincluded in mass storage device(s) 208 to enable reading from and/orwriting to the various computer readable media. Mass storage device(s)208 include removable media 226 and/or non-removable media.

I/O device(s) 210 include various devices that allow data and/or otherinformation to be input to or retrieved from computing device 200.Example I/O device(s) 210 include cursor control devices, keyboards,keypads, microphones, monitors or other display devices, speakers,printers, network interface cards, modems, lenses, CCDs or other imagecapture devices, and the like.

Display device 230 includes any type of device capable of displayinginformation to one or more users of computing device 200. Examples ofdisplay device 230 include a monitor, display terminal, video projectiondevice, and the like.

Interface(s) 206 include various interfaces that allow computing device200 to interact with other systems, devices, or computing environments.Example interface(s) 206 include any number of different networkinterfaces 220, such as interfaces to local area networks (LANs), widearea networks (WANs), wireless networks, and the Internet. Otherinterface(s) include user interface 218 and peripheral device interface222. The interface(s) 206 may also include one or more peripheralinterfaces such as interfaces for printers, pointing devices (mice,track pad, etc.), keyboards, and the like.

Bus 212 allows processor(s) 202, memory device(s) 204, interface(s) 206,mass storage device(s) 208, I/O device(s) 210, and display device 230 tocommunicate with one another, as well as other devices or componentscoupled to bus 212. Bus 212 represents one or more of several types ofbus structures, such as a system bus, PCI bus, IEEE 1394 bus, USB bus,and so forth.

For purposes of illustration, programs and other executable programcomponents are shown herein as discrete blocks, although it isunderstood that such programs and components may reside at various timesin different storage components of computing device 200, and areexecuted by processor(s) 202. Alternatively, the systems and proceduresdescribed herein can be implemented in hardware, or a combination ofhardware, software, and/or firmware. For example, one or moreapplication specific integrated circuits (ASICs) can be programmed tocarry out one or more of the systems and procedures described herein.

Referring to FIG. 3, the illustrated method 300 may be executed by thecontroller 102 or by the controller 102 in cooperation with the serversystem 120. For example, sensor outputs may be processed locallyaccording to the method 300 or transmitted to the server system 120 forprocessing.

The method 300 may include capturing 302 outputs of the one or moreinterior microphones 118 b and the one or more exterior microphones 106b. The output of the one or more exterior microphones 106 b may besubtracted 304 from the output of the one or more interior microphones118 b. The outputs of one or both of the microphones 106 b, 118 b may bescaled prior to subtracting in order to account for differences insensitivities and for dampening of sound passing from outside of thevehicle to the interior.

Where there are multiple exterior microphones, the output of eachmicrophones 106 b may be subtracted from the output of the microphone118 b closest to that microphone 106 b. Alternatively, an average of theoutputs of the microphones 106 b may be subtracted from the outputs ofall of the microphones 118 b.

The result of step 304 is a first difference signal in which thecontribution of sounds exterior to the vehicle is reduced relative tothe original output of the one or more interior microphones 118 b. Aninfotainment audio signal may then be subtracted 306 from the firstdifference signal to obtain a second difference signal. The infotainmentaudio signal may be, or be derived from, signals coupled to speakerswithin the interior of the vehicle. This reduces the impact of soundemitted by interior speakers of the vehicle. The magnitude of theinfotainment audio signal may be scaled according to a predeterminedvalue prior to subtracting in order to more closely cancel out theinfotainment audio signal from the second difference signal.

The second difference signal may be input 308 to an unsupervised anomalydetection model. In particular, the second difference signal may beinput into an unsupervised machine learning algorithm that trains amodel according to the signal over time. The unsupervised machinelearning algorithm may be any unsupervised machine learning algorithmknown in the art. The result of step 308 is a model that outputs whethera given signal is anomalous or not. In particular, the unsupervisedanomaly detection model may detect audible anomalies indicating distress(e.g., shouting, screaming, impacts, etc.).

The method 300 may further include inputting 310 the second differencesignal to a keyword detection algorithm. In some applications, apassenger in the vehicle may specify that a particular keyword shall beconsidered to indicate an anomaly. The keyword may be any arbitrary wordor phrase selected by the user to function as keyword, e.g., “apple,”“How are the flowers growing,” or the like. This word may be input asspeech or as text to the controller 102, such as prior to commencementof a ride in the vehicle. Accordingly, step 310 may include identifyingwords in the second difference signal using any speech recognitionapproach known in the art.

The method 300 may include evaluating 312 whether an anomaly is detectedin the second difference signal. An anomaly may be determined 312 to bedetected if either of (a) the unsupervised machine learning modelindicates an anomaly in the second difference signal and (b) thepredefined keyword is detected in the second difference signal. If ananomaly is not found, the method 300 may end, i.e. subsequent outputs ofthe microphone 118 b may be evaluated according to the method 300.

If an anomaly is determined 312 to be detected, then various actions maybe taken. For example, an alert may be transmitted 314 to a humanoperator. Such as a human operator in data communication with the serversystem 120. The alert may be in the form of an email, text, orapplication-specific alert output on a computing device used by thehuman dispatcher, such as a computing device having some or all of theattributes of the computing device 200.

The method 300 may further include streaming 316 outputs of one or bothof the camera 118 a and the one or more microphones 118 b to the humanoperator, e.g. the same computing device to which the alert wastransmitted 312 or a different computing device. Streaming 316 mayinclude streaming the portion of the outputs of the one or moremicrophones 118 b that were evaluated at step 302-312 and which wasfound to indicate an anomaly at step 312. The output of the one or moremicrophones 118 b streamed at step 316 may include the second differencesignal derived from the output of the one or more microphones 118 b.

If an input is found 318 to be received from the human operator thatindicates that the anomaly is dismissed, then the method 300 may end.The method 300 may then be repeated for subsequent outputs of the one ormore microphone 118 b. The input may be received from the same computingdevice to which the alert was transmitted or a different computingdevice.

If not, the method 300 may include receiving and executing 320 aninstruction from the human operator, such as from the same computingdevice to which the alert was transmitted 314 or a different computingdevice. Examples of instructions may include an instruction to stop,turn, slow down, proceed to an alternative destination, proceed to ahospital or other provider of emergency services, or any other change tothe operation and trajectory of the vehicle. The controller 102 thenexecutes the instruction by stopping, turning, slowing, or otherwiseproceeding autonomously to the destination specified in the instruction.

Referring to FIG. 4, given sufficient data regarding detected anomaliesand operator instructions provided in response to them (either dismissalor modifications of the vehicle operation or destination), a machinelearning model may be trained to autonomously invoke actions based ondetected anomalies.

For example, the illustrated method 400 may include storing 402 sensordata for a region of time in which an anomaly was detected, e.g., 30seconds, 1 minute, or some other interval, before and after the anomalywas detected. Sensor data may include outputs of interior sensors 116and exterior sensors 104. The method 400 may further include storing 404human operator responses to each anomaly (dismissed, stop, turn, slow,reroute, destination of reroute, etc.).

The data stored at steps 402 and 404 may then be used to train 406 amachine learning model with the sensor data for an anomaly being theinput and the dispatcher response to the anomaly being the desiredoutput for that sensor data. For example, many thousands, or tens ofthousands, of anomalies and their corresponding operator responses maybe processed in order to train the machine learning model to replicatethe response of the human operator. The machine learning model may beany machine learning model known in the art such as a deep neuralnetwork, decision tree, clustering, Bayesian network, genetic, or othertype of machine learning model.

The method 400 may then include processing 408 subsequent anomaliesaccording to the machine learning model. For example, in the method 300,if an anomaly is detected, the sensor data (interior sensors 116 andexterior sensors 104) may be input to the machine learning model. Thecontroller 102 may invoke whatever action is indicated by the machinelearning model.

In the above disclosure, reference has been made to the accompanyingdrawings, which form a part hereof, and in which is shown by way ofillustration specific implementations in which the disclosure may bepracticed. It is understood that other implementations may be utilizedand structural changes may be made without departing from the scope ofthe present disclosure. References in the specification to “oneembodiment,” “an embodiment,” “an example embodiment,” etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

Implementations of the systems, devices, and methods disclosed hereinmay comprise or utilize a special purpose or general-purpose computerincluding computer hardware, such as, for example, one or moreprocessors and system memory, as discussed herein. Implementationswithin the scope of the present disclosure may also include physical andother computer-readable media for carrying or storingcomputer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arecomputer storage media (devices). Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, implementations of the disclosure cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media (devices) and transmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,solid state drives (“SSDs”) (e.g., based on RAM), Flash memory,phase-change memory (“PCM”), other types of memory, other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.

An implementation of the devices, systems, and methods disclosed hereinmay communicate over a computer network. A “network” is defined as oneor more data links that enable the transport of electronic data betweencomputer systems and/or modules and/or other electronic devices. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a transmission medium. Transmissions media can include anetwork and/or data links, which can be used to carry desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. Combinations of the above should also be includedwithin the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, or even source code.Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, an in-dash vehicle computer, personalcomputers, desktop computers, laptop computers, message processors,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, mobile telephones, PDAs, tablets, pagers, routers, switches,various storage devices, and the like. The disclosure may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. Certain terms are usedthroughout the description and claims to refer to particular systemcomponents. As one skilled in the art will appreciate, components may bereferred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

It should be noted that the sensor embodiments discussed above maycomprise computer hardware, software, firmware, or any combinationthereof to perform at least a portion of their functions. For example, asensor may include computer code configured to be executed in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

At least some embodiments of the disclosure have been directed tocomputer program products comprising such logic (e.g., in the form ofsoftware) stored on any computer useable medium. Such software, whenexecuted in one or more data processing devices, causes a device tooperate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the disclosure.Thus, the breadth and scope of the present disclosure should not belimited by any of the above-described exemplary embodiments, but shouldbe defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the disclosure.

The invention claimed is:
 1. A system for a vehicle comprising: aninterior microphone sensing an interior of the vehicle; an exteriormicrophone; and a controller coupled to the interior microphone andprogrammed to— autonomously cause the vehicle to traverse a trajectory;receive an output of the interior microphone during autonomoustraversing of the trajectory; if an output of the interior microphoneindicates an interior anomaly, alert a remote human dispatcher; subtractan output of the exterior microphone from the output of the interiormicrophone to obtain a first difference signal; subtract an output of aninfotainment audio system from the first difference signal to obtain asecond difference signal; and if the second difference signal indicatesthe interior anomaly, alert the remote human dispatcher.
 2. The systemof claim 1, wherein the controller is further programmed to: train anunsupervised machine learning model according to the output of theinterior microphone over time; and if the unsupervised machine learningmodel indicates that the output of the interior microphone indicates theanomaly, alert the remote human dispatcher.
 3. The system of claim 1,wherein the controller is further programmed to: perform speechrecognition with respect to the output of the interior microphone; andif a result of speech recognition with respect to the output of theinterior microphone includes a predetermined keyword, alert the remotehuman dispatcher.
 4. The system of claim 1, wherein the controller isfurther programmed to: if an instruction is received from the humandispatcher, alter the trajectory of the vehicle according to theinstruction.
 5. The system of claim 1, wherein the controller is furtherprogrammed to, if the output of the interior microphone indicates theinterior anomaly, transmit the output of the interior microphone to theremote human dispatcher.
 6. The system of claim 1, further comprising acamera, a field of view of the camera including the interior of thevehicle; wherein the controller is further programmed to, if the outputof the interior microphone indicates the interior anomaly, transmit theoutput of the interior microphone and an output of the camera to theremote human dispatcher.
 7. The system of claim 1, further comprising: asteering actuator; a brake actuator; and an accelerator actuator;wherein the controller is further programmed to autonomously cause thevehicle to traverse the trajectory by activating the steering actuator,brake actuator, and accelerator actuator.
 8. The system of claim 1,further comprising: a Radio Detection and Ranging (RADAR) sensor; and aLight Detection and Ranging (LIDAR) sensor; wherein the controller isfurther programmed to autonomously cause the vehicle to traverse thetrajectory according to outputs of the RADAR sensor and LIDAR sensor. 9.A method comprising, by a controller of a vehicle: receiving an outputof an interior microphone sensing an interior of the vehicle;autonomously causing the vehicle to traverse a trajectory; determiningthat the output of the interior microphone indicates an interioranomaly; in response to determining that the output of the interiormicrophone indicates an interior anomaly, transmitting an alert to aremote human dispatcher; receiving, by the controller, an output from anexterior microphone; subtracting, by the controller, the output of theexterior microphone from the output of the interior microphone to obtaina first difference signal; subtracting, by the controller, an output ofan infotainment audio system from the first difference signal to obtaina second difference signal; processing the second difference signalaccording to an anomaly detection algorithm; determining that an outputof the anomaly detection algorithm indicates an interior anomaly; and inresponse to determining that the output of the anomaly detectionalgorithm indicates the interior anomaly, transmitting, by thecontroller, the alert to the remote human dispatcher.
 10. The method ofclaim 9, further comprising: training, by the controller, anunsupervised machine learning model according to the output of theinterior microphone over time; determining, by the controller, that theunsupervised machine learning model indicates that the output of theinterior microphone indicates the anomaly; and in response todetermining that the unsupervised machine learning model indicates thatthe output of the interior microphone indicates the anomaly,transmitting, by the controller, the alert to the remote humandispatcher.
 11. The method of claim 9, further comprising: performing,by the controller, speech recognition with respect to the output of theinterior microphone; determining, by the controller, that a result ofspeech recognition with respect to the output of the interior microphoneincludes a predetermined keyword; and in response to determining that aresult of speech recognition with respect to the output of the interiormicrophone includes the predetermined keyword, transmitting the alert tothe human dispatcher.
 12. The method of claim 9, further comprising:receiving, by the controller, an instruction from the human dispatcher;and in response to the instruction from the human dispatcher, changing,by the controller, the trajectory of the vehicle according to theinstruction.
 13. The method of claim 9, further comprising: receiving,by the controller, an output of a camera, a field of view of the cameraincluding the interior of the vehicle; and in response to determiningthat the output of the interior microphone indicates an interioranomaly, transmitting the output of the interior microphone and theoutput of the camera to the remote human dispatcher.
 14. The method ofclaim 9, further comprising autonomously causing, by the controller, thevehicle to traverse the trajectory by activating a steering actuator, abrake actuator, and an accelerator actuator.
 15. The method of claim 9,further comprising autonomously causing, by the controller, the vehicleto traverse the trajectory according to outputs of a radio detection andranging (RADAR) sensor and a light detection and ranging (LIDAR) sensor.